NiaPy.algorithms.basic.de.CrowdingDifferentialEvolution.setParameters() - Code Metrics - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

CrowdingDifferentialEvolution.setParameters() A
last analyzed 2020-11-16 11:17 UTC

↳ Parent: NiaPy.algorithms.basic.de
Complexity

Conditions
Size

Total Lines	12
Code Lines	3
Duplication

Lines	0
Ratio	0 %
Importance

Changes
Metric	Value
cc	1
eloc	3
nop	3
dl	0
loc	12
rs	10
c	0
b	0
f	0
# encoding=utf8
import logging
import math

from numpy import random as rand, argmin, argmax, mean, cos, asarray, append, sin, isfinite
from scipy.spatial.distance import euclidean

from NiaPy.algorithms.algorithm import Algorithm, Individual, defaultIndividualInit
from NiaPy.util.utility import objects2array

__all__ = ['DifferentialEvolution', 'DynNpDifferentialEvolution', 'AgingNpDifferentialEvolution', 'CrowdingDifferentialEvolution', 'MultiStrategyDifferentialEvolution', 'DynNpMultiStrategyDifferentialEvolution', 'AgingNpMultiMutationDifferentialEvolution', 'AgingIndividual', 'CrossRand1', 'CrossBest2', 'CrossBest1', 'CrossBest2', 'CrossCurr2Rand1', 'CrossCurr2Best1', 'multiMutations']

logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.basic')
logger.setLevel('INFO')

def CrossRand1(pop, ic, x_b, f, cr, rnd=rand, *args):

	r"""Mutation strategy with crossover.

	Mutation strategy uses three different random individuals from population to perform mutation.

	Mutation:
		Name: DE/rand/1

		:math:`\mathbf{x}_{r_1, G} + F \cdot (\mathbf{x}_{r_2, G} - \mathbf{x}_{r_3, G}`
		where :math:`r_1, r_2, r_3` are random indexes representing current population individuals.

	Crossover:
		Name: Binomial crossover

		:math:`\mathbf{x}_{i, G+1} = \begin{cases} \mathbf{u}_{i, G+1}, & \text{if $f(\mathbf{u}_{i, G+1}) \leq f(\mathbf{x}_{i, G})$}, \\ \mathbf{x}_{i, G}, & \text{otherwise}. \end{cases}`

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of individual being mutated.
		x_b (Individual): Current global best individual.
		f (float): Scale factor.
		cr (float): Crossover probability.
		rnd (mtrand.RandomState): Random generator.
		args (list): Additional arguments.

	Returns:
		numpy.ndarray: Mutated and mixed individual.
	"""
	j = rnd.randint(len(pop[ic]))
	p = [1 / (len(pop) - 1.0) if i != ic else 0 for i in range(len(pop))] if len(pop) > 3 else None
	r = rnd.choice(len(pop), 3, replace=not len(pop) >= 3, p=p)
	x = [pop[r[0]][i] + f * (pop[r[1]][i] - pop[r[2]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
	return asarray(x)

def CrossBest1(pop, ic, x_b, f, cr, rnd=rand, *args):

	r"""Mutation strategy with crossover.

	Mutation strategy uses two different random individuals from population and global best individual.

	Mutation:
		Name: de/best/1

		:math:`\mathbf{v}_{i, G} = \mathbf{x}_{best, G} + F \cdot (\mathbf{x}_{r_1, G} - \mathbf{x}_{r_2, G})`
		where :math:`r_1, r_2` are random indexes representing current population individuals.

	Crossover:
		Name: Binomial crossover

		:math:`\mathbf{x}_{i, G+1} = \begin{cases} \mathbf{u}_{i, G+1}, & \text{if $f(\mathbf{u}_{i, G+1}) \leq f(\mathbf{x}_{i, G})$}, \\ \mathbf{x}_{i, G}, & \text{otherwise}. \end{cases}`

	args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of individual being mutated.
		x_b (Individual): Current global best individual.
		f (float): Scale factor.
		cr (float): Crossover probability.
		rnd (mtrand.RandomState): Random generator.
		args (list): Additional arguments.

	returns:
		numpy.ndarray: Mutated and mixed individual.
	"""
	j = rnd.randint(len(pop[ic]))
	p = [1 / (len(pop) - 1.0) if i != ic else 0 for i in range(len(pop))] if len(pop) > 2 else None
	r = rnd.choice(len(pop), 2, replace=not len(pop) >= 2, p=p)
	x = [x_b[i] + f * (pop[r[0]][i] - pop[r[1]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
	return asarray(x)

def CrossRand2(pop, ic, x_b, f, cr, rnd=rand, *args):

	r"""Mutation strategy with crossover.

	Mutation strategy uses five different random individuals from population.

	Mutation:
		Name: de/best/1

		:math:`\mathbf{v}_{i, G} = \mathbf{x}_{r_1, G} + F \cdot (\mathbf{x}_{r_2, G} - \mathbf{x}_{r_3, G}) + F \cdot (\mathbf{x}_{r_4, G} - \mathbf{x}_{r_5, G})`
		where :math:`r_1, r_2, r_3, r_4, r_5` are random indexes representing current population individuals.

	Crossover:
		Name: Binomial crossover

		:math:`\mathbf{x}_{i, G+1} = \begin{cases} \mathbf{u}_{i, G+1}, & \text{if $f(\mathbf{u}_{i, G+1}) \leq f(\mathbf{x}_{i, G})$}, \\ \mathbf{x}_{i, G}, & \text{otherwise}. \end{cases}`

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of individual being mutated.
		x_b (Individual): Current global best individual.
		f (float): Scale factor.
		cr (float): Crossover probability.
		rnd (mtrand.RandomState): Random generator.
		args (list): Additional arguments.

	Returns:
		numpy.ndarray: mutated and mixed individual.
	"""
	j = rnd.randint(len(pop[ic]))
	p = [1 / (len(pop) - 1.0) if i != ic else 0 for i in range(len(pop))] if len(pop) > 5 else None
	r = rnd.choice(len(pop), 5, replace=not len(pop) >= 5, p=p)
	x = [pop[r[0]][i] + f * (pop[r[1]][i] - pop[r[2]][i]) + f * (pop[r[3]][i] - pop[r[4]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
	return asarray(x)

def CrossBest2(pop, ic, x_b, f, cr, rnd=rand, *args):

	r"""Mutation strategy with crossover.

	Mutation:
		Name: de/best/2

		:math:`\mathbf{v}_{i, G} = \mathbf{x}_{best, G} + F \cdot (\mathbf{x}_{r_1, G} - \mathbf{x}_{r_2, G}) + F \cdot (\mathbf{x}_{r_3, G} - \mathbf{x}_{r_4, G})`
		where :math:`r_1, r_2, r_3, r_4` are random indexes representing current population individuals.

	Crossover:
		Name: Binomial crossover

		:math:`\mathbf{x}_{i, G+1} = \begin{cases} \mathbf{u}_{i, G+1}, & \text{if $f(\mathbf{u}_{i, G+1}) \leq f(\mathbf{x}_{i, G})$}, \\ \mathbf{x}_{i, G}, & \text{otherwise}. \end{cases}`

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of individual being mutated.
		x_b (Individual): Current global best individual.
		f (float): Scale factor.
		cr (float): Crossover probability.
		rnd (mtrand.RandomState): Random generator.
		args (list): Additional arguments.

	Returns:
		numpy.ndarray: mutated and mixed individual.
	"""
	j = rnd.randint(len(pop[ic]))
	p = [1 / (len(pop) - 1.0) if i != ic else 0 for i in range(len(pop))] if len(pop) > 4 else None
	r = rnd.choice(len(pop), 4, replace=not len(pop) >= 4, p=p)
	x = [x_b[i] + f * (pop[r[0]][i] - pop[r[1]][i]) + f * (pop[r[2]][i] - pop[r[3]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
	return asarray(x)

def CrossCurr2Rand1(pop, ic, x_b, f, cr, rnd=rand, *args):

	r"""Mutation strategy with crossover.

	Mutation:
		Name: de/curr2rand/1

		:math:`\mathbf{v}_{i, G} = \mathbf{x}_{i, G} + F \cdot (\mathbf{x}_{r_1, G} - \mathbf{x}_{r_2, G}) + F \cdot (\mathbf{x}_{r_3, G} - \mathbf{x}_{r_4, G})`
		where :math:`r_1, r_2, r_3, r_4` are random indexes representing current population individuals

	Crossover:
		Name: Binomial crossover

		:math:`\mathbf{x}_{i, G+1} = \begin{cases} \mathbf{u}_{i, G+1}, & \text{if $f(\mathbf{u}_{i, G+1}) \leq f(\mathbf{x}_{i, G})$}, \\ \mathbf{x}_{i, G}, & \text{otherwise}. \end{cases}`

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of individual being mutated.
		x_b (Individual): Current global best individual.
		f (float): Scale factor.
		cr (float): Crossover probability.
		rnd (mtrand.RandomState): Random generator.
		args (list): Additional arguments.

	Returns:
		numpy.ndarray: mutated and mixed individual.
	"""
	j = rnd.randint(len(pop[ic]))
	p = [1 / (len(pop) - 1.0) if i != ic else 0 for i in range(len(pop))] if len(pop) > 4 else None
	r = rnd.choice(len(pop), 4, replace=not len(pop) >= 4, p=p)
	x = [pop[ic][i] + f * (pop[r[0]][i] - pop[r[1]][i]) + f * (pop[r[2]][i] - pop[r[3]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
	return asarray(x)

def CrossCurr2Best1(pop, ic, x_b, f, cr, rnd=rand, **kwargs):
	r"""Mutation strategy with crossover.

	Mutation:
		Name: de/curr-to-best/1

		:math:`\mathbf{v}_{i, G} = \mathbf{x}_{i, G} + F \cdot (\mathbf{x}_{r_1, G} - \mathbf{x}_{r_2, G}) + F \cdot (\mathbf{x}_{r_3, G} - \mathbf{x}_{r_4, G})`
		where :math:`r_1, r_2, r_3, r_4` are random indexes representing current population individuals

	Crossover:
		Name: Binomial crossover

		:math:`\mathbf{x}_{i, G+1} = \begin{cases} \mathbf{u}_{i, G+1}, & \text{if $f(\mathbf{u}_{i, G+1}) \leq f(\mathbf{x}_{i, G})$}, \\ \mathbf{x}_{i, G}, & \text{otherwise}. \end{cases}`

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of individual being mutated.
		x_b (Individual): Current global best individual.
		f (float): Scale factor.
		cr (float): Crossover probability.
		rnd (mtrand.RandomState): Random generator.
		args (list): Additional arguments.

	Returns:
		numpy.ndarray: mutated and mixed individual.
	"""
	j = rnd.randint(len(pop[ic]))
	p = [1 / (len(pop) - 1.0) if i != ic else 0 for i in range(len(pop))] if len(pop) > 3 else None
	r = rnd.choice(len(pop), 3, replace=not len(pop) >= 3, p=p)
	x = [pop[ic][i] + f * (x_b[i] - pop[r[0]][i]) + f * (pop[r[1]][i] - pop[r[2]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
	return asarray(x)

class DifferentialEvolution(Algorithm):
	r"""Implementation of Differential evolution algorithm.

	Algorithm:
	 	Differential evolution algorithm

	Date:
		2018

	Author:
		Uros Mlakar and Klemen Berkovič

	License:
		MIT

	Reference paper:
		Storn, Rainer, and Kenneth Price. "Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces." Journal of global optimization 11.4 (1997): 341-359.

	Attributes:
		Name (List[str]): List of string of names for algorithm.
		F (float): Scale factor.
		CR (float): Crossover probability.
		CrossMutt (Callable[numpy.ndarray, int, numpy.ndarray, float, float, mtrand.RandomState, Dict[str, Any]]): crossover and mutation strategy.

	See Also:
		* :class:`NiaPy.algorithms.Algorithm`
	"""
	Name = ['DifferentialEvolution', 'DE']

	@staticmethod
	def algorithmInfo():
		r"""Get basic information of algorithm.

		Returns:
			str: Basic information of algorithm.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		return r"""Storn, Rainer, and Kenneth Price. "Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces." Journal of global optimization 11.4 (1997): 341-359."""

	@staticmethod

	def typeParameters():
		r"""Get dictionary with functions for checking values of parameters.

		Returns:
			Dict[str, Callable]:
				* F (Callable[[Union[float, int]], bool]): Check for correct value of parameter.
				* CR (Callable[[float], bool]): Check for correct value of parameter.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.typeParameters`
		"""
		d = Algorithm.typeParameters()
		d.update({
			'F': lambda x: isinstance(x, (float, int)) and 0 < x <= 2,
			'CR': lambda x: isinstance(x, float) and 0 <= x <= 1
		})
		return d

	def setParameters(self, NP=50, F=1, CR=0.8, CrossMutt=CrossRand1, **ukwargs):
		r"""Set the algorithm parameters.

		Arguments:
			NP (Optional[int]): Population size.
			F (Optional[float]): Scaling factor.
			CR (Optional[float]): Crossover rate.
			CrossMutt (Optional[Callable[[numpy.ndarray, int, numpy.ndarray, float, float, mtrand.RandomState, list], numpy.ndarray]]): Crossover and mutation strategy.
			ukwargs (Dict[str, Any]): Additional arguments.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.setParameters`
		"""
		Algorithm.setParameters(self, NP=NP, InitPopFunc=ukwargs.pop('InitPopFunc', defaultIndividualInit), itype=ukwargs.pop('itype', Individual), **ukwargs)
		self.F, self.CR, self.CrossMutt = F, CR, CrossMutt

	def getParameters(self):
		r"""Get parameters values of the algorithm.

		Returns:
			Dict[str, Any]: TODO

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.getParameters`
		"""
		d = Algorithm.getParameters(self)
		d.update({
			'F': self.F,
			'CR': self.CR,
			'CrossMutt': self.CrossMutt
		})
		return d

	def evolve(self, pop, xb, task, **kwargs):
		r"""Evolve population.

		Args:
			pop (numpy.ndarray): Current population.
			xb (Individual): Current best individual.
			task (Task): Optimization task.
			**kwargs (Dict[str, Any]): Additional arguments.

		Returns:
			numpy.ndarray: New evolved populations.
		"""
		return objects2array([self.itype(x=self.CrossMutt(pop, i, xb, self.F, self.CR, self.Rand), task=task, rnd=self.Rand, e=True) for i in range(len(pop))])

	def selection(self, pop, npop, xb, fxb, task, **kwargs):
		r"""Operator for selection.

		Args:
			pop (numpy.ndarray): Current population.
			npop (numpy.ndarray): New Population.
			xb (numpy.ndarray): Current global best solution.
			fxb (float): Current global best solutions fitness/objective value.
			task (Task): Optimization task.
			**kwargs (Dict[str, Any]): Additional arguments.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, float]:
				1. New selected individuals.
				2. New global best solution.
				3. New global best solutions fitness/objective value.
		"""
		arr = objects2array([e if e.f < pop[i].f else pop[i] for i, e in enumerate(npop)])
		xb, fxb = self.getBest(arr, asarray([e.f for e in arr]), xb, fxb)
		return arr, xb, fxb

	def postSelection(self, pop, task, xb, fxb, **kwargs):
		r"""Apply additional operation after selection.

		Args:
			pop (numpy.ndarray): Current population.
			task (Task): Optimization task.
			xb (numpy.ndarray): Global best solution.
			**kwargs (Dict[str, Any]): Additional arguments.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, float]:
				1. New population.
				2. New global best solution.
				3. New global best solutions fitness/objective value.
		"""
		return pop, xb, fxb

	def runIteration(self, task, pop, fpop, xb, fxb, **dparams):
		r"""Core function of Differential Evolution algorithm.

		Args:
			task (Task): Optimization task.
			pop (numpy.ndarray): Current population.
			fpop (numpy.ndarray): Current populations fitness/function values.
			xb (numpy.ndarray): Current best individual.
			fxb (float): Current best individual function/fitness value.
			**dparams (dict): Additional arguments.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
				1. New population.
				2. New population fitness/function values.
				3. New global best solution.
				4. New global best solutions fitness/objective value.
				5. Additional arguments.

		See Also:
			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.evolve`
			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.selection`
			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.postSelection`
		"""
		npop = self.evolve(pop, xb, task)
		pop, xb, fxb = self.selection(pop, npop, xb, fxb, task=task)
		pop, xb, fxb = self.postSelection(pop, task, xb, fxb)
		fpop = asarray([x.f for x in pop])
		xb, fxb = self.getBest(pop, fpop, xb, fxb)
		return pop, fpop, xb, fxb, {}

class CrowdingDifferentialEvolution(DifferentialEvolution):
	r"""Implementation of Differential evolution algorithm with multiple mutation strateys.

	Algorithm:
		Implementation of Differential evolution algorithm with multiple mutation strateys

	Date:
		2018

	Author:
		Klemen Berkovič

	License:
		MIT

	Attributes:
		Name (List[str]): List of strings representing algorithm name.
		CrowPop (float): Proportion of range for cowding.

	See Also:
		* :class:`NiaPy.algorithms.basic.DifferentialEvolution`
	"""
	Name = ['CrowdingDifferentialEvolution', 'CDE']

	@staticmethod
	def algorithmInfo():
		r"""Get basic information of algorithm.

		Returns:
			str: Basic information of algorithm.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		return r"""No New"""

	def setParameters(self, CrowPop=0.1, **ukwargs):
		r"""Set core parameters of algorithm.

		Args:
			CrowPop (Optional[float]): Crowding distance.
			**ukwargs: Additional arguments.

		See Also:
			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.setParameters`
		"""
		DifferentialEvolution.setParameters(self, **ukwargs)
		self.CrowPop = CrowPop

	def selection(self, pop, npop, xb, fxb, task, **kwargs):
		r"""Operator for selection of individuals.

		Args:
			pop (numpy.ndarray): Current population.
			npop (numpy.ndarray): New population.
			xb (numpy.ndarray): Current global best solution.
			fxb (float): Current global best solutions fitness/objective value.
			task (Task): Optimization task.
			kwargs (Dict[str, Any]): Additional arguments.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, float]:
				1. New population.
				2. New global best solution.
				3. New global best solutions fitness/objective value.
		"""
		P = []
		for e in npop:
			i = argmin([euclidean(e, f) for f in pop])
			P.append(pop[i] if pop[i].f < e.f else e)
		return asarray(P), xb, fxb

class DynNpDifferentialEvolution(DifferentialEvolution):
	r"""Implementation of Dynamic poulation size Differential evolution algorithm.

	Algorithm:
		Dynamic poulation size Differential evolution algorithm

	Date:
		2018

	Author:
		Klemen Berkovič

	License:
		MIT

	Attributes:
		Name (List[str]): List of strings representing algorithm names.
		pmax (int): Number of population reductions.
		rp (int): Small non-negative number which is added to value of generations.

	See Also:
		* :class:`NiaPy.algorithms.basic.DifferentialEvolution`
	"""
	Name = ['DynNpDifferentialEvolution', 'dynNpDE']

	@staticmethod
	def algorithmInfo():
		r"""Get basic information of algorithm.

		Returns:
			str: Basic information of algorithm.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		return r"""No info"""

	@staticmethod
	def typeParameters():
		r"""Get dictionary with functions for checking values of parameters.

		Returns:
			Dict[str, Callable]:
				* rp (Callable[[Union[float, int]], bool])
				* pmax (Callable[[int], bool])

		See Also:
			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.typeParameters`
		"""
		r = DifferentialEvolution.typeParameters()
		r['rp'] = lambda x: isinstance(x, (float, int)) and x > 0
		r['pmax'] = lambda x: isinstance(x, int) and x > 0
		return r

	def setParameters(self, pmax=50, rp=3, **ukwargs):
		r"""Set the algorithm parameters.

		Arguments:
			pmax (Optional[int]): umber of population reductions.
			rp (Optional[int]): Small non-negative number which is added to value of generations.

		See Also:
			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.setParameters`
		"""
		DifferentialEvolution.setParameters(self, **ukwargs)
		self.pmax, self.rp = pmax, rp

	def postSelection(self, pop, task, xb, fxb, **kwargs):
		r"""Post selection operator.

		In this algorithm the post selection operator decrements the population at specific iterations/generations.

		Args:
			pop (numpy.ndarray): Current population.
			task (Task): Optimization task.
			kwargs (Dict[str, Any]): Additional arguments.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, float]:
				1. Changed current population.
				2. New global best solution.
				3. New global best solutions fitness/objective value.
		"""
		Gr = task.nFES // (self.pmax * len(pop)) + self.rp
		nNP = len(pop) // 2
		if task.Iters == Gr and len(pop) > 3: pop = objects2array([pop[i] if pop[i].f < pop[i + nNP].f else pop[i + nNP] for i in range(nNP)])
		return pop, xb, fxb

def proportional(Lt_min, Lt_max, mu, x_f, avg, **args):
	r"""Proportional calculation of age of individual.

	Args:
		Lt_min (int): Minimal life time.
		Lt_max (int): Maximal life time.
		mu (float): Median of life time.
		x_f (float): Individuals function/fitness value.
		avg (float): Average fitness/function value of current population.
		args (list): Additional arguments.

	Returns:
		int: Age of individual.
	"""
	proportional_result = Lt_max if math.isinf(avg) else Lt_min + mu * avg / x_f
	return min(proportional_result, Lt_max)

def linear(Lt_min, mu, x_f, x_gw, x_gb, **args):
	r"""Linear calculation of age of individual.

	Args:
		Lt_min (int): Minimal life time.
		Lt_max (int): Maximal life time.
		mu (float): Median of life time.
		x_f (float): Individual function/fitness value.
		avg (float): Average fitness/function value.
		x_gw (float): Global worst fitness/function value.
		x_gb (float): Global best fitness/function value.
		args (list): Additional arguments.

	Returns:
		int: Age of individual.
	"""
	return Lt_min + 2 * mu * (x_f - x_gw) / (x_gb - x_gw)

def bilinear(Lt_min, Lt_max, mu, x_f, avg, x_gw, x_gb, **args):
	r"""Bilinear calculation of age of individual.

	Args:
		Lt_min (int): Minimal life time.
		Lt_max (int): Maximal life time.
		mu (float): Median of life time.
		x_f (float): Individual function/fitness value.
		avg (float): Average fitness/function value.
		x_gw (float): Global worst fitness/function value.
		x_gb (float): Global best fitness/function value.
		args (list): Additional arguments.

	Returns:
		int: Age of individual.
	"""
	if avg < x_f: return Lt_min + mu * (x_f - x_gw) / (x_gb - x_gw)
	return 0.5 * (Lt_min + Lt_max) + mu * (x_f - avg) / (x_gb - avg)

class AgingIndividual(Individual):
	r"""Individual with aging.

	Attributes:
		age (int): Age of individual.

	See Also:
		* :class:`NiaPy.algorithms.Individual`
	"""
	age = 0

	def __init__(self, **kwargs):
		r"""Init Aging Individual.

		Args:
			**kwargs (Dict[str, Any]): Additional arguments sent to parent.

		See Also:
			* :func:`NiaPy.algorithms.Individual.__init__`
		"""
		Individual.__init__(self, **kwargs)
		self.age = 0

class AgingNpDifferentialEvolution(DifferentialEvolution):
	r"""Implementation of Differential evolution algorithm with aging individuals.

	Algorithm:
		Differential evolution algorithm with dynamic population size that is defined by the quality of population

	Date:
		2018

	Author:
		Klemen Berkovič

	License:
		MIT

	Attributes:
		Name (List[str]): list of strings representing algorithm names.
		Lt_min (int): Minimal age of individual.
		Lt_max (int): Maximal age of individual.
		delta_np (float): Proportion of how many individuals shall die.
		omega (float): Acceptance rate for individuals to die.
		mu (int): Mean of individual max and min age.
		age (Callable[[int, int, float, float, float, float, float], int]): Function for calculation of age for individual.

	See Also:
		* :class:`NiaPy.algorithms.basic.DifferentialEvolution`
	"""
	Name = ['AgingNpDifferentialEvolution', 'ANpDE']

	@staticmethod
	def algorithmInfo():
		r"""Get basic information of algorithm.

		Returns:
			str: Basic information of algorithm.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		return r"""No info"""

	@staticmethod
	def typeParameters():
		r"""Get dictionary with functions for checking values of parameters.

		Returns:
			Dict[str, Callable]:
				* Lt_min (Callable[[int], bool])
				* Lt_max (Callable[[int], bool])
				* delta_np (Callable[[float], bool])
				* omega (Callable[[float], bool])

		See Also:
			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.typeParameters`
		"""
		r = DifferentialEvolution.typeParameters()
		r.update({
			'Lt_min': lambda x: isinstance(x, int) and x >= 0,
			'Lt_max': lambda x: isinstance(x, int) and x >= 0,
			'delta_np': lambda x: isinstance(x, float) and 0 <= x <= 1,
			'omega': lambda x: isinstance(x, float) and 1 >= x >= 0
		})
		return r

	def setParameters(self, Lt_min=0, Lt_max=12, delta_np=0.3, omega=0.3, age=proportional, CrossMutt=CrossBest1, **ukwargs):
		r"""Set the algorithm parameters.

		Arguments:
			Lt_min (Optional[int]): Minimum life time.
			Lt_max (Optional[int]): Maximum life time.
			age (Optional[Callable[[int, int, float, float, float, float, float], int]]): Function for calculation of age for individual.

		See Also:
			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.setParameters`
		"""
		DifferentialEvolution.setParameters(self, itype=AgingIndividual, **ukwargs)
		self.Lt_min, self.Lt_max, self.age, self.delta_np, self.omega = Lt_min, Lt_max, age, delta_np, omega
		self.mu = abs(self.Lt_max - self.Lt_min) / 2

	def deltaPopE(self, t):
		r"""Calculate how many individuals are going to dye.

		Args:
			t (int): Number of generations made by the algorithm.

		Returns:
			int: Number of individuals to dye.
		"""
		return int(self.delta_np * abs(sin(t)))

	def deltaPopC(self, t):
		r"""Calculate how many individuals are going to be created.

		Args:
			t (int): Number of generations made by the algorithm.

		Returns:
			int: Number of individuals to be born.
		"""
		return int(self.delta_np * abs(cos(t)))

	def aging(self, task, pop):
		r"""Apply aging to individuals.

		Args:
			task (Task): Optimization task.
			pop (numpy.ndarray[Individual]): Current population.

		Returns:
			numpy.ndarray[Individual]: New population.
		"""
		fpop = asarray([x.f for x in pop])
		x_b, x_w = pop[argmin(fpop)], pop[argmax(fpop)]
		avg, npop = mean(fpop[isfinite(fpop)]), []
		for x in pop:
			x.age += 1
			Lt = round(self.age(Lt_min=self.Lt_min, Lt_max=self.Lt_max, mu=self.mu, x_f=x.f, avg=avg, x_gw=x_w.f, x_gb=x_b.f))
			if x.age <= Lt: npop.append(x)
		if len(npop) == 0: npop = objects2array([self.itype(task=task, rnd=self.Rand, e=True) for _ in range(self.NP)])
		return npop

	def popIncrement(self, pop, task):
		r"""Increment population.

		Args:
			pop (numpy.ndarray[Individual]): Current population.
			task (Task): Optimization task.

		Returns:
			numpy.ndarray[Individual]: Increased population.
		"""
		deltapop = int(round(max(1, self.NP * self.deltaPopE(task.Iters))))
		return objects2array([self.itype(task=task, rnd=self.Rand, e=True) for _ in range(deltapop)])

	def popDecrement(self, pop, task):
		r"""Decrement population.

		Args:
			pop (numpy.ndarray): Current population.
			task (Task): Optimization task.

		Returns:
			numpy.ndarray[Individual]: Decreased population.
		"""
		deltapop = int(round(max(1, self.NP * self.deltaPopC(task.Iters))))
		if len(pop) - deltapop <= 0: return pop
		ni = self.Rand.choice(len(pop), deltapop, replace=False)
		npop = []
		for i, e in enumerate(pop):
			if i not in ni: npop.append(e)
			elif self.rand() >= self.omega: npop.append(e)
		return objects2array(npop)

	def selection(self, pop, npop, xb, fxb, task, **kwargs):
		r"""Select operator for individuals with aging.

		Args:
			pop (numpy.ndarray): Current population.
			npop (numpy.ndarray): New population.
			xb (numpy.ndarray): Current global best solution.
			fxb (float): Current global best solutions fitness/objective value.
			task (Task): Optimization task.
			**kwargs (Dict[str, Any]): Additional arguments.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, float]:
				1. New population of individuals.
				2. New global best solution.
				3. New global best solutions fitness/objective value.
		"""
		npop, xb, fxb = DifferentialEvolution.selection(self, pop, npop, xb, fxb, task)
		npop = append(npop, self.popIncrement(pop, task))
		xb, fxb = self.getBest(npop, asarray([e.f for e in npop]), xb, fxb)
		pop = self.aging(task, npop)
		return pop, xb, fxb

	def postSelection(self, pop, task, xb, fxb, **kwargs):
		r"""Post selection operator.

		Args:
			pop (numpy.ndarray): Current population.
			task (Task): Optimization task.
			xb (Individual): Global best individual.
			**kwargs (Dict[str, Any]): Additional arguments.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, float]:
			1. New population.
			2. New global best solution
			3. New global best solutions fitness/objective value
		"""
		return self.popDecrement(pop, task) if len(pop) > self.NP else pop, xb, fxb

def multiMutations(pop, i, xb, F, CR, rnd, task, itype, strategies, **kwargs):
	r"""Mutation strategy that takes more than one strategy and applys them to individual.

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		i (int): Index of current individual.
		xb (Individual): Current best individual.
		F (float): Scale factor.
		CR (float): Crossover probability.
		rnd (mtrand.RandomState): Random generator.
		task (Task): Optimization task.
		IndividualType (Individual): Individual type used in algorithm.
		strategies (Iterable[Callable[[numpy.ndarray[Individual], int, Individual, float, float, mtrand.RandomState], numpy.ndarray[Individual]]]): List of mutation strategies.
		**kwargs (Dict[str, Any]): Additional arguments.

	Returns:
		Individual: Best individual from applyed mutations strategies.
	"""
	L = [itype(x=strategy(pop, i, xb, F, CR, rnd=rnd), task=task, e=True, rnd=rnd) for strategy in strategies]
	return L[argmin([x.f for x in L])]

class MultiStrategyDifferentialEvolution(DifferentialEvolution):
	r"""Implementation of Differential evolution algorithm with multiple mutation strateys.

	Algorithm:
		Implementation of Differential evolution algorithm with multiple mutation strateys

	Date:
		2018

	Author:
		Klemen Berkovič

	License:
		MIT

	Attributes:
		Name (List[str]): List of strings representing algorithm names.
		strategies (Iterable[Callable[[numpy.ndarray[Individual], int, Individual, float, float, mtrand.RandomState], numpy.ndarray[Individual]]]): List of mutation strategies.
		CrossMutt (Callable[[numpy.ndarray[Individual], int, Individual, float, float, Task, Individual, Iterable[Callable[[numpy.ndarray, int, numpy.ndarray, float, float, mtrand.RandomState, Dict[str, Any]], Individual]]], Individual]): Multi crossover and mutation combiner function.

	See Also:
		* :class:`NiaPy.algorithms.basic.DifferentialEvolution`
	"""
	Name = ['MultiStrategyDifferentialEvolution', 'MsDE']

	@staticmethod
	def algorithmInfo():
		r"""Get basic information of algorithm.

		Returns:
			str: Basic information of algorithm.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		return r"""No info"""

	@staticmethod
	def typeParameters():
		r"""Get dictionary with functions for checking values of parameters.

		Returns:
			Dict[str, Callable]: Testing functions for parameters.

		See Also:
			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.typeParameters`
		"""
		r = DifferentialEvolution.typeParameters()
		r.pop('CrossMutt', None)
		r.update({'strategies': lambda x: callable(x)})
		return r

	def setParameters(self, strategies=(CrossRand1, CrossBest1, CrossCurr2Best1, CrossRand2), **ukwargs):
		r"""Set the arguments of the algorithm.

		Arguments:
			strategies (Optional[Iterable[Callable[[numpy.ndarray[Individual], int, Individual, float, float, mtrand.RandomState], numpy.ndarray[Individual]]]]): List of mutation strategyis.
			CrossMutt (Optional[Callable[[numpy.ndarray[Individual], int, Individual, float, float, Task, Individual, Iterable[Callable[[numpy.ndarray, int, numpy.ndarray, float, float, mtrand.RandomState, Dict[str, Any]], Individual]]], Individual]]): Multi crossover and mutation combiner function.

		See Also:
			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.setParameters`
		"""
		DifferentialEvolution.setParameters(self, CrossMutt=multiMutations, **ukwargs)
		self.strategies = strategies

	def getParameters(self):
		r"""Get parameters values of the algorithm.

		Returns:
			Dict[str, Any]: TODO.

		See Also:
			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.getParameters`
		"""
		d = DifferentialEvolution.getParameters(self)
		d.update({'strategies': self.strategies})
		return d

	def evolve(self, pop, xb, task, **kwargs):

		r"""Evolve population with the help multiple mutation strategies.

		Args:
			pop (numpy.ndarray): Current population.
			xb (numpy.ndarray): Current best individual.
			task (Task): Optimization task.
			**kwargs (Dict[str, Any]): Additional arguments.

		Returns:
			numpy.ndarray: New population of individuals.
		"""
		return objects2array([self.CrossMutt(pop, i, xb, self.F, self.CR, self.Rand, task, self.itype, self.strategies) for i in range(len(pop))])

class DynNpMultiStrategyDifferentialEvolution(MultiStrategyDifferentialEvolution, DynNpDifferentialEvolution):
	r"""Implementation of Dynamic population size Differential evolution algorithm with dynamic population size that is defined by the quality of population.

	Algorithm:
		Dynamic population size Differential evolution algorithm with dynamic population size that is defined by the quality of population

	Date:
		2018

	Author:
		Klemen Berkovič

	License:
		MIT

	Attributes:
		Name (List[str]): List of strings representing algorithm name.

	See Also:
		* :class:`NiaPy.algorithms.basic.MultiStrategyDifferentialEvolution`
		* :class:`NiaPy.algorithms.basic.DynNpDifferentialEvolution`
	"""
	Name = ['DynNpMultiStrategyDifferentialEvolution', 'dynNpMsDE']

	@staticmethod
	def algorithmInfo():
		r"""Get basic information of algorithm.

		Returns:
			str: Basic information of algorithm.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		return r"""No info"""

	@staticmethod
	def typeParameters():
		r"""Get dictionary with functions for checking values of parameters.

		Returns:
			Dict[str, Callable]:
				* rp (Callable[[Union[float, int]], bool]): TODO
				* pmax (Callable[[int], bool]): TODO

		See Also:
			* :func:`NiaPy.algorithms.basic.MultiStrategyDifferentialEvolution.typeParameters`
		"""
		r = MultiStrategyDifferentialEvolution.typeParameters()
		r['rp'] = lambda x: isinstance(x, (float, int)) and x > 0
		r['pmax'] = lambda x: isinstance(x, int) and x > 0
		return r

	def setParameters(self, **ukwargs):
		r"""Set the arguments of the algorithm.

		Args:
			ukwargs (Dict[str, Any]): Additional arguments.

		See Also:
			* :func:`NiaPy.algorithms.basic.MultiStrategyDifferentialEvolution.setParameters`
			* :func:`NiaPy.algorithms.basic.DynNpDifferentialEvolution.setParameters`
		"""
		DynNpDifferentialEvolution.setParameters(self, **ukwargs)
		MultiStrategyDifferentialEvolution.setParameters(self, **ukwargs)

	def evolve(self, pop, xb, task, **kwargs):
		r"""Evolve the current population.

		Args:
			pop (numpy.ndarray): Current population.
			xb (numpy.ndarray): Global best solution.
			task (Task): Optimization task.
			**kwargs (dict): Additional arguments.

		Returns:
			numpy.ndarray: Evolved new population.
		"""
		return MultiStrategyDifferentialEvolution.evolve(self, pop, xb, task, **kwargs)

	def postSelection(self, pop, task, xb, fxb, **kwargs):
		r"""Post selection operator.

		Args:
			pop (numpy.ndarray): Current population.
			task (Task): Optimization task.
			**kwargs (Dict[str, Any]): Additional arguments.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, float]:
				1. New population.
				2. New global best solution.
				3. New global best solutions fitness/objective value.

		See Also:
			* :func:`NiaPy.algorithms.basic.DynNpDifferentialEvolution.postSelection`
		"""
		return DynNpDifferentialEvolution.postSelection(self, pop, task, xb, fxb)

class AgingNpMultiMutationDifferentialEvolution(AgingNpDifferentialEvolution, MultiStrategyDifferentialEvolution):
	r"""Implementation of Differential evolution algorithm with aging individuals.

	Algorithm:
		Differential evolution algorithm with dynamic population size that is defined by the quality of population

	Date:
		2018

	Author:
		Klemen Berkovič

	License:
		MIT

	Attributes:
		Name (List[str]): List of strings representing algorithm names

	See Also:
		* :class:`NiaPy.algorithms.basic.AgingNpDifferentialEvolution`
		* :class:`NiaPy.algorithms.basic.MultiStrategyDifferentialEvolution`
	"""
	Name = ['AgingNpMultiMutationDifferentialEvolution', 'ANpMSDE']

	@staticmethod
	def algorithmInfo():
		r"""Get basic information of algorithm.

		Returns:
			str: Basic information of algorithm.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		return r"""No info"""

	@staticmethod
	def typeParameters():
		r"""Get dictionary with functions for checking values of parameters.

		Returns:
			Dict[str, Callable]: Mappings form parameter names to test functions.

		See Also:
			* :func:`NiaPy.algorithms.basic.MultiStrategyDifferentialEvolution.typeParameters`
			* :func:`NiaPy.algorithms.basic.AgingNpDifferentialEvolution.typeParameters`
		"""
		d = AgingNpDifferentialEvolution.typeParameters()
		d.update(MultiStrategyDifferentialEvolution.typeParameters())
		return d

	def setParameters(self, **ukwargs):
		r"""Set core parameter arguments.

		Args:
			**ukwargs (Dict[str, Any]): Additional arguments.

		See Also:
			* :func:`NiaPy.algorithms.basic.AgingNpDifferentialEvolution.setParameters`
			* :func:`NiaPy.algorithms.basic.MultiStrategyDifferentialEvolution.setParameters`
		"""
		AgingNpDifferentialEvolution.setParameters(self, **ukwargs)
		MultiStrategyDifferentialEvolution.setParameters(self, stratgeys=(CrossRand1, CrossBest1, CrossCurr2Rand1, CrossRand2), itype=AgingIndividual, **ukwargs)

	def evolve(self, pop, xb, task, **kwargs):
		r"""Evolve current population.

		Args:
			pop (numpy.ndarray): Current population.
			xb (numpy.ndarray): Global best individual.
			task (Task): Optimization task.
			**kwargs (Dict[str, Any]): Additional arguments.

		Returns:
			numpy.ndarray: New population of individuals.
		"""
		return MultiStrategyDifferentialEvolution.evolve(self, pop, xb, task, **kwargs)

# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3

NiaOrg / NiaPy

CrowdingDifferentialEvolution.setParameters() A last analyzed 2020-11-16 11:17 UTC

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like

CrowdingDifferentialEvolution.setParameters() A
last analyzed 2020-11-16 11:17 UTC
